Overview

Dataset statistics

Number of variables35
Number of observations468
Missing cells6276
Missing cells (%)38.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory128.1 KiB
Average record size in memory280.3 B

Variable types

Numeric12
Categorical23

Warnings

Name English has a high cardinality: 284 distinct values High cardinality
Position has a high cardinality: 135 distinct values High cardinality
Ban_Total is highly correlated with Ban_G.P. and 15 other fieldsHigh correlation
Ban_G.P. is highly correlated with Ban_Total and 12 other fieldsHigh correlation
Eng_Total is highly correlated with Ban_Total and 11 other fieldsHigh correlation
Eng_G.P. is highly correlated with Ban_Total and 8 other fieldsHigh correlation
Phy_Total is highly correlated with Ban_Total and 14 other fieldsHigh correlation
Phy_G.P. is highly correlated with Ban_Total and 14 other fieldsHigh correlation
Che_Total is highly correlated with Ban_Total and 16 other fieldsHigh correlation
Che_G.P. is highly correlated with Ban_Total and 15 other fieldsHigh correlation
BIO_Total is highly correlated with Ban_Total and 17 other fieldsHigh correlation
BIO_G.P. is highly correlated with Ban_Total and 15 other fieldsHigh correlation
Math_Total is highly correlated with Ban_Total and 14 other fieldsHigh correlation
Math_G.P. is highly correlated with Phy_Total and 12 other fieldsHigh correlation
ICT_Total is highly correlated with Ban_Total and 16 other fieldsHigh correlation
ICt_G.P. is highly correlated with Ban_Total and 15 other fieldsHigh correlation
APT is highly correlated with Ban_Total and 16 other fieldsHigh correlation
APT_G.P. is highly correlated with Phy_Total and 10 other fieldsHigh correlation
Grand Total is highly correlated with Ban_Total and 17 other fieldsHigh correlation
TTL_GP is highly correlated with Ban_Total and 17 other fieldsHigh correlation
No.s of F is highly correlated with Ban_Total and 17 other fieldsHigh correlation
Ban_Total is highly correlated with Ban_G.P. and 16 other fieldsHigh correlation
Ban_G.P. is highly correlated with Ban_Total and 16 other fieldsHigh correlation
Eng_Total is highly correlated with Ban_Total and 11 other fieldsHigh correlation
Eng_G.P. is highly correlated with Ban_Total and 8 other fieldsHigh correlation
Phy_Total is highly correlated with Ban_Total and 15 other fieldsHigh correlation
Phy_G.P. is highly correlated with Ban_Total and 15 other fieldsHigh correlation
Che_Total is highly correlated with Ban_Total and 16 other fieldsHigh correlation
Che_G.P. is highly correlated with Ban_Total and 15 other fieldsHigh correlation
BIO_Total is highly correlated with Ban_Total and 17 other fieldsHigh correlation
BIO_G.P. is highly correlated with Ban_Total and 16 other fieldsHigh correlation
Math_Total is highly correlated with Ban_Total and 15 other fieldsHigh correlation
Math_G.P. is highly correlated with Phy_Total and 13 other fieldsHigh correlation
ICT_Total is highly correlated with Ban_Total and 17 other fieldsHigh correlation
ICt_G.P. is highly correlated with Ban_Total and 17 other fieldsHigh correlation
APT is highly correlated with Ban_Total and 16 other fieldsHigh correlation
APT_G.P. is highly correlated with Ban_Total and 15 other fieldsHigh correlation
Grand Total is highly correlated with Ban_Total and 17 other fieldsHigh correlation
TTL_GP is highly correlated with Ban_Total and 17 other fieldsHigh correlation
No.s of F is highly correlated with Ban_Total and 17 other fieldsHigh correlation
Ban_Total is highly correlated with Ban_G.P. and 5 other fieldsHigh correlation
Ban_G.P. is highly correlated with Ban_Total and 7 other fieldsHigh correlation
Eng_Total is highly correlated with Eng_G.P. and 3 other fieldsHigh correlation
Eng_G.P. is highly correlated with Eng_Total and 3 other fieldsHigh correlation
Phy_Total is highly correlated with Phy_G.P. and 13 other fieldsHigh correlation
Phy_G.P. is highly correlated with Phy_Total and 13 other fieldsHigh correlation
Che_Total is highly correlated with Phy_Total and 11 other fieldsHigh correlation
Che_G.P. is highly correlated with Phy_Total and 12 other fieldsHigh correlation
BIO_Total is highly correlated with Ban_Total and 11 other fieldsHigh correlation
BIO_G.P. is highly correlated with Ban_Total and 13 other fieldsHigh correlation
Math_Total is highly correlated with Phy_Total and 9 other fieldsHigh correlation
Math_G.P. is highly correlated with Phy_Total and 6 other fieldsHigh correlation
ICT_Total is highly correlated with Ban_G.P. and 10 other fieldsHigh correlation
ICt_G.P. is highly correlated with Ban_G.P. and 11 other fieldsHigh correlation
APT is highly correlated with Phy_Total and 6 other fieldsHigh correlation
APT_G.P. is highly correlated with Phy_Total and 5 other fieldsHigh correlation
Grand Total is highly correlated with Ban_Total and 17 other fieldsHigh correlation
TTL_GP is highly correlated with Ban_Total and 17 other fieldsHigh correlation
No.s of F is highly correlated with Ban_Total and 15 other fieldsHigh correlation
ATP_Position is highly correlated with Che_G.P. and 20 other fieldsHigh correlation
Che_G.P. is highly correlated with ATP_Position and 22 other fieldsHigh correlation
Eng_G.P. is highly correlated with ATP_Position and 15 other fieldsHigh correlation
Section is highly correlated with Ban_G.P. and 2 other fieldsHigh correlation
APT_L.G. is highly correlated with ATP_Position and 12 other fieldsHigh correlation
BIO_Total is highly correlated with Che_G.P. and 25 other fieldsHigh correlation
Math_G.P. is highly correlated with ATP_Position and 18 other fieldsHigh correlation
Ban_G.P. is highly correlated with ATP_Position and 22 other fieldsHigh correlation
TTL_GP is highly correlated with ATP_Position and 25 other fieldsHigh correlation
APT_G.P. is highly correlated with ATP_Position and 12 other fieldsHigh correlation
Phy_G.P. is highly correlated with ATP_Position and 20 other fieldsHigh correlation
Eng_Total is highly correlated with Che_G.P. and 17 other fieldsHigh correlation
Sec_Position is highly correlated with ATP_Position and 24 other fieldsHigh correlation
ICT_L.G. is highly correlated with Che_G.P. and 21 other fieldsHigh correlation
Math_Total is highly correlated with ATP_Position and 26 other fieldsHigh correlation
4th Sub is highly correlated with Section and 2 other fieldsHigh correlation
Che_L.G. is highly correlated with ATP_Position and 21 other fieldsHigh correlation
Ban_Total is highly correlated with ATP_Position and 23 other fieldsHigh correlation
Che_Total is highly correlated with Che_G.P. and 25 other fieldsHigh correlation
ICt_G.P. is highly correlated with Che_G.P. and 21 other fieldsHigh correlation
ID No. is highly correlated with Section and 1 other fieldsHigh correlation
ICT_Total is highly correlated with ATP_Position and 26 other fieldsHigh correlation
APT is highly correlated with ATP_Position and 21 other fieldsHigh correlation
Grand Total is highly correlated with ATP_Position and 28 other fieldsHigh correlation
TTL_L.G. is highly correlated with ATP_Position and 26 other fieldsHigh correlation
BIO_G.P. is highly correlated with ATP_Position and 23 other fieldsHigh correlation
Phy_L.G. is highly correlated with ATP_Position and 24 other fieldsHigh correlation
Ban_L.G. is highly correlated with ATP_Position and 21 other fieldsHigh correlation
Phy_Total is highly correlated with ATP_Position and 24 other fieldsHigh correlation
Math_L.G. is highly correlated with ATP_Position and 22 other fieldsHigh correlation
BIO_L.G. is highly correlated with BIO_Total and 22 other fieldsHigh correlation
Eng_L.G. is highly correlated with Eng_G.P. and 12 other fieldsHigh correlation
No.s of F is highly correlated with Che_G.P. and 20 other fieldsHigh correlation
ATP_Position is highly correlated with APT_L.G. and 1 other fieldsHigh correlation
Che_G.P. is highly correlated with Che_L.G. High correlation
Eng_G.P. is highly correlated with Eng_L.G. High correlation
Section is highly correlated with 4th SubHigh correlation
APT_L.G. is highly correlated with ATP_Position and 1 other fieldsHigh correlation
Math_G.P. is highly correlated with TTL_L.G. and 1 other fieldsHigh correlation
Ban_G.P. is highly correlated with Ban_L.G. High correlation
APT_G.P. is highly correlated with ATP_Position and 1 other fieldsHigh correlation
Phy_G.P. is highly correlated with Phy_L.G. High correlation
Sec_Position is highly correlated with TTL_L.G.High correlation
ICT_L.G. is highly correlated with ICt_G.P. and 1 other fieldsHigh correlation
4th Sub is highly correlated with SectionHigh correlation
Che_L.G. is highly correlated with Che_G.P.High correlation
ICt_G.P. is highly correlated with ICT_L.G. High correlation
TTL_L.G. is highly correlated with Math_G.P. and 2 other fieldsHigh correlation
BIO_G.P. is highly correlated with BIO_L.G. High correlation
Phy_L.G. is highly correlated with Phy_G.P.High correlation
Ban_L.G. is highly correlated with Ban_G.P.High correlation
Math_L.G. is highly correlated with Math_G.P. and 1 other fieldsHigh correlation
BIO_L.G. is highly correlated with ICT_L.G. and 1 other fieldsHigh correlation
Eng_L.G. is highly correlated with Eng_G.P.High correlation
ID No. has 179 (38.2%) missing values Missing
Name English has 183 (39.1%) missing values Missing
Section has 179 (38.2%) missing values Missing
4th Sub has 179 (38.2%) missing values Missing
Ban_Total has 179 (38.2%) missing values Missing
Ban_G.P. has 179 (38.2%) missing values Missing
Ban_L.G. has 179 (38.2%) missing values Missing
Eng_Total has 179 (38.2%) missing values Missing
Eng_G.P. has 179 (38.2%) missing values Missing
Eng_L.G. has 179 (38.2%) missing values Missing
Phy_Total has 179 (38.2%) missing values Missing
Phy_G.P. has 179 (38.2%) missing values Missing
Phy_L.G. has 179 (38.2%) missing values Missing
Che_Total has 179 (38.2%) missing values Missing
Che_G.P. has 179 (38.2%) missing values Missing
Che_L.G. has 179 (38.2%) missing values Missing
BIO_Total has 179 (38.2%) missing values Missing
BIO_G.P. has 179 (38.2%) missing values Missing
BIO_L.G. has 179 (38.2%) missing values Missing
Math_Total has 179 (38.2%) missing values Missing
Math_G.P. has 179 (38.2%) missing values Missing
Math_L.G. has 179 (38.2%) missing values Missing
ICT_Total has 179 (38.2%) missing values Missing
ICt_G.P. has 179 (38.2%) missing values Missing
ICT_L.G. has 179 (38.2%) missing values Missing
APT has 186 (39.7%) missing values Missing
APT_G.P. has 179 (38.2%) missing values Missing
APT_L.G. has 179 (38.2%) missing values Missing
Grand Total has 179 (38.2%) missing values Missing
TTL_GP has 179 (38.2%) missing values Missing
TTL_L.G. has 179 (38.2%) missing values Missing
No.s of F has 179 (38.2%) missing values Missing
Sec_Position has 179 (38.2%) missing values Missing
Position has 179 (38.2%) missing values Missing
ATP_Position has 179 (38.2%) missing values Missing
Name English is uniformly distributed Uniform
TTL_GP has 153 (32.7%) zeros Zeros
No.s of F has 136 (29.1%) zeros Zeros

Reproduction

Analysis started2021-07-04 14:12:40.387762
Analysis finished2021-07-04 14:13:05.048096
Duration24.66 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

ID No.
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct289
Distinct (%)100.0%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean1297390.201
Minimum1190001
Maximum2190033
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:05.140873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1190001
5-th percentile1190016.4
Q11190079
median1190152
Q31190227
95-th percentile2190017.6
Maximum2190033
Range1000032
Interquartile range (IQR)148

Descriptive statistics

Standard deviation309951.3797
Coefficient of variation (CV)0.2389037465
Kurtosis4.541619574
Mean1297390.201
Median Absolute Deviation (MAD)74
Skewness2.551516488
Sum374945768
Variance9.606985775 × 1010
MonotonicityNot monotonic
2021-07-04T20:13:05.276740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21900101
 
0.2%
11900671
 
0.2%
11902131
 
0.2%
11901651
 
0.2%
11902321
 
0.2%
11900371
 
0.2%
11900221
 
0.2%
11902431
 
0.2%
11900841
 
0.2%
21900321
 
0.2%
Other values (279)279
59.6%
(Missing)179
38.2%
ValueCountFrequency (%)
11900011
0.2%
11900021
0.2%
11900031
0.2%
11900041
0.2%
11900051
0.2%
11900061
0.2%
11900071
0.2%
11900081
0.2%
11900091
0.2%
11900101
0.2%
ValueCountFrequency (%)
21900331
0.2%
21900321
0.2%
21900311
0.2%
21900301
0.2%
21900291
0.2%
21900281
0.2%
21900271
0.2%
21900261
0.2%
21900251
0.2%
21900231
0.2%

Name English
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct284
Distinct (%)99.6%
Missing183
Missing (%)39.1%
Memory size3.8 KiB
SHOBNOM SABIHA
 
2
BADHON ROY
 
1
RAJOWONE SHARIAR
 
1
RABBI ISLAM JOY
 
1
MUBTASIM FUAD
 
1
Other values (279)
279 

Length

Max length28
Median length17
Mean length17.16842105
Min length5

Characters and Unicode

Total characters4893
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique283 ?
Unique (%)99.3%

Sample

1st rowSAIMA SHARMIN MONISHA
2nd rowMOSSAMAT NUSAIBA ISLAM
3rd rowTAHMINA AFROJ RITU
4th rowANUPOMA DEBSHARMA
5th rowMARIA AKTER MIM

Common Values

ValueCountFrequency (%)
SHOBNOM SABIHA2
 
0.4%
BADHON ROY1
 
0.2%
RAJOWONE SHARIAR1
 
0.2%
RABBI ISLAM JOY1
 
0.2%
MUBTASIM FUAD1
 
0.2%
MUSFEKA IFFAT SENIN1
 
0.2%
MD. MARUF HASAN MUNNA1
 
0.2%
MD. NUR ISTYAK LIKHAN1
 
0.2%
FARHAT MUBASSHIRA1
 
0.2%
KAMLESH ROY1
 
0.2%
Other values (274)274
58.5%
(Missing)183
39.1%

Length

2021-07-04T20:13:05.542032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
md100
 
12.0%
islam20
 
2.4%
roy14
 
1.7%
akter14
 
1.7%
hasan14
 
1.7%
mst11
 
1.3%
rahman11
 
1.3%
most10
 
1.2%
tasnim7
 
0.8%
al7
 
0.8%
Other values (521)626
75.1%

Most occurring characters

ValueCountFrequency (%)
A806
16.5%
549
11.2%
M373
 
7.6%
I346
 
7.1%
S309
 
6.3%
R274
 
5.6%
H261
 
5.3%
N261
 
5.3%
D210
 
4.3%
T178
 
3.6%
Other values (38)1326
27.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4107
83.9%
Space Separator549
 
11.2%
Other Punctuation127
 
2.6%
Lowercase Letter106
 
2.2%
Dash Punctuation4
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A806
19.6%
M373
9.1%
I346
 
8.4%
S309
 
7.5%
R274
 
6.7%
H261
 
6.4%
N261
 
6.4%
D210
 
5.1%
T178
 
4.3%
U174
 
4.2%
Other values (15)915
22.3%
Lowercase Letter
ValueCountFrequency (%)
a17
16.0%
i10
 
9.4%
s8
 
7.5%
l7
 
6.6%
n7
 
6.6%
r7
 
6.6%
h7
 
6.6%
u6
 
5.7%
o6
 
5.7%
d5
 
4.7%
Other values (10)26
24.5%
Space Separator
ValueCountFrequency (%)
549
100.0%
Other Punctuation
ValueCountFrequency (%)
.127
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4213
86.1%
Common680
 
13.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A806
19.1%
M373
 
8.9%
I346
 
8.2%
S309
 
7.3%
R274
 
6.5%
H261
 
6.2%
N261
 
6.2%
D210
 
5.0%
T178
 
4.2%
U174
 
4.1%
Other values (35)1021
24.2%
Common
ValueCountFrequency (%)
549
80.7%
.127
 
18.7%
-4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A806
16.5%
549
11.2%
M373
 
7.6%
I346
 
7.1%
S309
 
6.3%
R274
 
5.6%
H261
 
5.3%
N261
 
5.3%
D210
 
4.3%
T178
 
3.6%
Other values (38)1326
27.1%

Section
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10
Distinct (%)3.5%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
Jagori
35 
Nirjor
34 
Muktodhara
33 
Chayatoru
32 
Sarnokamal
32 
Other values (5)
123 

Length

Max length11
Median length9
Mean length8.529411765
Min length6

Characters and Unicode

Total characters2465
Distinct characters25
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShankhamala
2nd rowShankhamala
3rd rowShankhamala
4th rowShankhamala
5th rowShankhamala

Common Values

ValueCountFrequency (%)
Jagori35
 
7.5%
Nirjor34
 
7.3%
Muktodhara33
 
7.1%
Chayatoru32
 
6.8%
Sarnokamal32
 
6.8%
Dhanshiri32
 
6.8%
Chayanot30
 
6.4%
Chayabithi30
 
6.4%
Shankhamala17
 
3.6%
Tapobon14
 
3.0%
(Missing)179
38.2%

Length

2021-07-04T20:13:05.755484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:05.831286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
jagori35
12.1%
nirjor34
11.8%
muktodhara33
11.4%
sarnokamal32
11.1%
chayatoru32
11.1%
dhanshiri32
11.1%
chayabithi30
10.4%
chayanot30
10.4%
shankhamala17
5.9%
tapobon14
 
4.8%

Most occurring characters

ValueCountFrequency (%)
a495
20.1%
h253
10.3%
r232
9.4%
o224
9.1%
i193
 
7.8%
n125
 
5.1%
t125
 
5.1%
C92
 
3.7%
y92
 
3.7%
k82
 
3.3%
Other values (15)552
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2176
88.3%
Uppercase Letter289
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a495
22.7%
h253
11.6%
r232
10.7%
o224
10.3%
i193
 
8.9%
n125
 
5.7%
t125
 
5.7%
y92
 
4.2%
k82
 
3.8%
u65
 
3.0%
Other values (8)290
13.3%
Uppercase Letter
ValueCountFrequency (%)
C92
31.8%
S49
17.0%
J35
 
12.1%
N34
 
11.8%
M33
 
11.4%
D32
 
11.1%
T14
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin2465
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a495
20.1%
h253
10.3%
r232
9.4%
o224
9.1%
i193
 
7.8%
n125
 
5.1%
t125
 
5.1%
C92
 
3.7%
y92
 
3.7%
k82
 
3.3%
Other values (15)552
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a495
20.1%
h253
10.3%
r232
9.4%
o224
9.1%
i193
 
7.8%
n125
 
5.1%
t125
 
5.1%
C92
 
3.7%
y92
 
3.7%
k82
 
3.3%
Other values (15)552
22.4%

4th Sub
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)1.0%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
Math
178 
Bio
80 
Agri
31 

Length

Max length4
Median length4
Mean length3.723183391
Min length3

Characters and Unicode

Total characters1076
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgri
2nd rowAgri
3rd rowAgri
4th rowAgri
5th rowAgri

Common Values

ValueCountFrequency (%)
Math178
38.0%
Bio80
17.1%
Agri31
 
6.6%
(Missing)179
38.2%

Length

2021-07-04T20:13:06.103492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:06.165978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
math178
61.6%
bio80
27.7%
agri31
 
10.7%

Most occurring characters

ValueCountFrequency (%)
M178
16.5%
a178
16.5%
t178
16.5%
h178
16.5%
i111
10.3%
B80
7.4%
o80
7.4%
A31
 
2.9%
g31
 
2.9%
r31
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter787
73.1%
Uppercase Letter289
 
26.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a178
22.6%
t178
22.6%
h178
22.6%
i111
14.1%
o80
10.2%
g31
 
3.9%
r31
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
M178
61.6%
B80
27.7%
A31
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1076
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M178
16.5%
a178
16.5%
t178
16.5%
h178
16.5%
i111
10.3%
B80
7.4%
o80
7.4%
A31
 
2.9%
g31
 
2.9%
r31
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M178
16.5%
a178
16.5%
t178
16.5%
h178
16.5%
i111
10.3%
B80
7.4%
o80
7.4%
A31
 
2.9%
g31
 
2.9%
r31
 
2.9%

Ban_Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct61
Distinct (%)21.1%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean62.06228374
Minimum0
Maximum91
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:06.259699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q153
median64
Q373
95-th percentile82.6
Maximum91
Range91
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.51494427
Coefficient of variation (CV)0.233877057
Kurtosis0.6413349415
Mean62.06228374
Median Absolute Deviation (MAD)10
Skewness-0.6441647735
Sum17936
Variance210.6836073
MonotonicityNot monotonic
2021-07-04T20:13:06.384648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6420
 
4.3%
6712
 
2.6%
6212
 
2.6%
5611
 
2.4%
6910
 
2.1%
759
 
1.9%
688
 
1.7%
498
 
1.7%
658
 
1.7%
797
 
1.5%
Other values (51)184
39.3%
(Missing)179
38.2%
ValueCountFrequency (%)
01
 
0.2%
222
0.4%
261
 
0.2%
271
 
0.2%
292
0.4%
311
 
0.2%
332
0.4%
343
0.6%
353
0.6%
363
0.6%
ValueCountFrequency (%)
911
 
0.2%
901
 
0.2%
892
 
0.4%
881
 
0.2%
861
 
0.2%
854
0.9%
843
0.6%
832
 
0.4%
826
1.3%
811
 
0.2%

Ban_G.P.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
3.5
83 
4.0
66 
3.0
60 
0.0
46 
5.0
34 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters867
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.5
2nd row4.0
3rd row4.0
4th row3.0
5th row3.5

Common Values

ValueCountFrequency (%)
3.583
17.7%
4.066
 
14.1%
3.060
 
12.8%
0.046
 
9.8%
5.034
 
7.3%
(Missing)179
38.2%

Length

2021-07-04T20:13:06.576252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:06.654358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.583
28.7%
4.066
22.8%
3.060
20.8%
0.046
15.9%
5.034
11.8%

Most occurring characters

ValueCountFrequency (%)
.289
33.3%
0252
29.1%
3143
16.5%
5117
13.5%
466
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number578
66.7%
Other Punctuation289
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0252
43.6%
3143
24.7%
5117
20.2%
466
 
11.4%
Other Punctuation
ValueCountFrequency (%)
.289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.289
33.3%
0252
29.1%
3143
16.5%
5117
13.5%
466
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.289
33.3%
0252
29.1%
3143
16.5%
5117
13.5%
466
 
7.6%

Ban_L.G.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.1%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
A-
83 
A
66 
B
60 
F
43 
A+
34 

Length

Max length3
Median length1
Mean length1.425605536
Min length1

Characters and Unicode

Total characters412
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-
2nd rowA
3rd rowA
4th rowB
5th rowA-

Common Values

ValueCountFrequency (%)
A-83
17.7%
A66
 
14.1%
B60
 
12.8%
F43
 
9.2%
A+34
 
7.3%
ABS3
 
0.6%
(Missing)179
38.2%

Length

2021-07-04T20:13:06.884891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:06.983607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a183
63.3%
b60
 
20.8%
f43
 
14.9%
abs3
 
1.0%

Most occurring characters

ValueCountFrequency (%)
A186
45.1%
-83
20.1%
B63
 
15.3%
F43
 
10.4%
+34
 
8.3%
S3
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter295
71.6%
Dash Punctuation83
 
20.1%
Math Symbol34
 
8.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A186
63.1%
B63
 
21.4%
F43
 
14.6%
S3
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
-83
100.0%
Math Symbol
ValueCountFrequency (%)
+34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin295
71.6%
Common117
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A186
63.1%
B63
 
21.4%
F43
 
14.6%
S3
 
1.0%
Common
ValueCountFrequency (%)
-83
70.9%
+34
29.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A186
45.1%
-83
20.1%
B63
 
15.3%
F43
 
10.4%
+34
 
8.3%
S3
 
0.7%

Eng_Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct65
Distinct (%)22.5%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean54.42214533
Minimum0
Maximum88
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:07.119273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.4
Q146
median57
Q365
95-th percentile76
Maximum88
Range88
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.44260186
Coefficient of variation (CV)0.2837558455
Kurtosis0.61201958
Mean54.42214533
Median Absolute Deviation (MAD)9
Skewness-0.6541760136
Sum15728
Variance238.4739523
MonotonicityNot monotonic
2021-07-04T20:13:07.239950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4915
 
3.2%
5412
 
2.6%
6011
 
2.4%
6210
 
2.1%
5910
 
2.1%
589
 
1.9%
579
 
1.9%
659
 
1.9%
638
 
1.7%
518
 
1.7%
Other values (55)188
40.2%
(Missing)179
38.2%
ValueCountFrequency (%)
01
0.2%
71
0.2%
101
0.2%
121
0.2%
132
0.4%
161
0.2%
192
0.4%
202
0.4%
221
0.2%
241
0.2%
ValueCountFrequency (%)
881
 
0.2%
872
 
0.4%
842
 
0.4%
822
 
0.4%
804
0.9%
792
 
0.4%
764
0.9%
751
 
0.2%
742
 
0.4%
735
1.1%

Eng_G.P.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
3.0
81 
0.0
80 
3.5
78 
4.0
37 
5.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters867
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.5
2nd row3.0
3rd row3.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
3.081
17.3%
0.080
17.1%
3.578
16.7%
4.037
 
7.9%
5.013
 
2.8%
(Missing)179
38.2%

Length

2021-07-04T20:13:07.714700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:07.791495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.081
28.0%
0.080
27.7%
3.578
27.0%
4.037
12.8%
5.013
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0291
33.6%
.289
33.3%
3159
18.3%
591
 
10.5%
437
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number578
66.7%
Other Punctuation289
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0291
50.3%
3159
27.5%
591
 
15.7%
437
 
6.4%
Other Punctuation
ValueCountFrequency (%)
.289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0291
33.6%
.289
33.3%
3159
18.3%
591
 
10.5%
437
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0291
33.6%
.289
33.3%
3159
18.3%
591
 
10.5%
437
 
4.3%

Eng_L.G.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.1%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
B
81 
A-
78 
F
77 
A
37 
A+
13 

Length

Max length3
Median length1
Mean length1.335640138
Min length1

Characters and Unicode

Total characters386
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-
2nd rowB
3rd rowB
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
B81
17.3%
A-78
16.7%
F77
16.5%
A37
 
7.9%
A+13
 
2.8%
ABS3
 
0.6%
(Missing)179
38.2%

Length

2021-07-04T20:13:08.076752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:08.188457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a128
44.3%
b81
28.0%
f77
26.6%
abs3
 
1.0%

Most occurring characters

ValueCountFrequency (%)
A131
33.9%
B84
21.8%
-78
20.2%
F77
19.9%
+13
 
3.4%
S3
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter295
76.4%
Dash Punctuation78
 
20.2%
Math Symbol13
 
3.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A131
44.4%
B84
28.5%
F77
26.1%
S3
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
-78
100.0%
Math Symbol
ValueCountFrequency (%)
+13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin295
76.4%
Common91
 
23.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A131
44.4%
B84
28.5%
F77
26.1%
S3
 
1.0%
Common
ValueCountFrequency (%)
-78
85.7%
+13
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII386
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A131
33.9%
B84
21.8%
-78
20.2%
F77
19.9%
+13
 
3.4%
S3
 
0.8%

Phy_Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct76
Distinct (%)26.3%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean59.20069204
Minimum8
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:08.332050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile26
Q144
median59
Q376
95-th percentile91.6
Maximum100
Range92
Interquartile range (IQR)32

Descriptive statistics

Standard deviation20.27984425
Coefficient of variation (CV)0.3425609322
Kurtosis-0.781296891
Mean59.20069204
Median Absolute Deviation (MAD)16
Skewness-0.1244528344
Sum17109
Variance411.2720829
MonotonicityNot monotonic
2021-07-04T20:13:08.479656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5710
 
2.1%
499
 
1.9%
448
 
1.7%
797
 
1.5%
767
 
1.5%
507
 
1.5%
687
 
1.5%
586
 
1.3%
646
 
1.3%
606
 
1.3%
Other values (66)216
46.2%
(Missing)179
38.2%
ValueCountFrequency (%)
81
 
0.2%
101
 
0.2%
183
0.6%
201
 
0.2%
221
 
0.2%
233
0.6%
242
0.4%
264
0.9%
274
0.9%
284
0.9%
ValueCountFrequency (%)
1001
 
0.2%
972
 
0.4%
952
 
0.4%
943
0.6%
934
0.9%
923
0.6%
913
0.6%
904
0.9%
885
1.1%
874
0.9%

Phy_G.P.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
0.0
85 
5.0
60 
3.0
57 
3.5
47 
4.0
40 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters867
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row5.0
4th row3.5
5th row5.0

Common Values

ValueCountFrequency (%)
0.085
18.2%
5.060
 
12.8%
3.057
 
12.2%
3.547
 
10.0%
4.040
 
8.5%
(Missing)179
38.2%

Length

2021-07-04T20:13:08.757434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:08.842207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.085
29.4%
5.060
20.8%
3.057
19.7%
3.547
16.3%
4.040
13.8%

Most occurring characters

ValueCountFrequency (%)
0327
37.7%
.289
33.3%
5107
 
12.3%
3104
 
12.0%
440
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number578
66.7%
Other Punctuation289
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0327
56.6%
5107
 
18.5%
3104
 
18.0%
440
 
6.9%
Other Punctuation
ValueCountFrequency (%)
.289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0327
37.7%
.289
33.3%
5107
 
12.3%
3104
 
12.0%
440
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0327
37.7%
.289
33.3%
5107
 
12.3%
3104
 
12.0%
440
 
4.6%

Phy_L.G.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.1%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
F
75 
A+
60 
B
56 
A-
47 
A
40 

Length

Max length3
Median length1
Mean length1.446366782
Min length1

Characters and Unicode

Total characters418
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA+
2nd rowA
3rd rowA+
4th rowA-
5th rowA+

Common Values

ValueCountFrequency (%)
F75
16.0%
A+60
 
12.8%
B56
 
12.0%
A-47
 
10.0%
A40
 
8.5%
ABS11
 
2.4%
(Missing)179
38.2%

Length

2021-07-04T20:13:09.180304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:09.295994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a147
50.9%
f75
26.0%
b56
 
19.4%
abs11
 
3.8%

Most occurring characters

ValueCountFrequency (%)
A158
37.8%
F75
17.9%
B67
16.0%
+60
 
14.4%
-47
 
11.2%
S11
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter311
74.4%
Math Symbol60
 
14.4%
Dash Punctuation47
 
11.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A158
50.8%
F75
24.1%
B67
21.5%
S11
 
3.5%
Math Symbol
ValueCountFrequency (%)
+60
100.0%
Dash Punctuation
ValueCountFrequency (%)
-47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin311
74.4%
Common107
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A158
50.8%
F75
24.1%
B67
21.5%
S11
 
3.5%
Common
ValueCountFrequency (%)
+60
56.1%
-47
43.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A158
37.8%
F75
17.9%
B67
16.0%
+60
 
14.4%
-47
 
11.2%
S11
 
2.6%

Che_Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)25.3%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean57.5017301
Minimum6
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:09.465541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile24
Q146
median58
Q372
95-th percentile84
Maximum91
Range85
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.218604
Coefficient of variation (CV)0.3168357538
Kurtosis-0.2519957962
Mean57.5017301
Median Absolute Deviation (MAD)13
Skewness-0.4102630609
Sum16618
Variance331.9175317
MonotonicityNot monotonic
2021-07-04T20:13:09.629104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5511
 
2.4%
5010
 
2.1%
7310
 
2.1%
659
 
1.9%
499
 
1.9%
468
 
1.7%
688
 
1.7%
588
 
1.7%
597
 
1.5%
567
 
1.5%
Other values (63)202
43.2%
(Missing)179
38.2%
ValueCountFrequency (%)
62
0.4%
121
0.2%
131
0.2%
151
0.2%
162
0.4%
171
0.2%
192
0.4%
201
0.2%
212
0.4%
231
0.2%
ValueCountFrequency (%)
913
0.6%
903
0.6%
892
 
0.4%
882
 
0.4%
872
 
0.4%
851
 
0.2%
843
0.6%
832
 
0.4%
823
0.6%
816
1.3%

Che_G.P.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
0.0
79 
3.0
68 
3.5
56 
4.0
48 
5.0
38 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters867
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.5
3rd row4.0
4th row0.0
5th row3.5

Common Values

ValueCountFrequency (%)
0.079
16.9%
3.068
 
14.5%
3.556
 
12.0%
4.048
 
10.3%
5.038
 
8.1%
(Missing)179
38.2%

Length

2021-07-04T20:13:09.887414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:09.955463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.079
27.3%
3.068
23.5%
3.556
19.4%
4.048
16.6%
5.038
13.1%

Most occurring characters

ValueCountFrequency (%)
0312
36.0%
.289
33.3%
3124
 
14.3%
594
 
10.8%
448
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number578
66.7%
Other Punctuation289
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0312
54.0%
3124
 
21.5%
594
 
16.3%
448
 
8.3%
Other Punctuation
ValueCountFrequency (%)
.289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0312
36.0%
.289
33.3%
3124
 
14.3%
594
 
10.8%
448
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0312
36.0%
.289
33.3%
3124
 
14.3%
594
 
10.8%
448
 
5.5%

Che_L.G.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.1%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
F
72 
B
68 
A-
56 
A
48 
A+
38 

Length

Max length3
Median length1
Mean length1.373702422
Min length1

Characters and Unicode

Total characters397
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA+
2nd rowA-
3rd rowA
4th rowF
5th rowA-

Common Values

ValueCountFrequency (%)
F72
15.4%
B68
 
14.5%
A-56
 
12.0%
A48
 
10.3%
A+38
 
8.1%
ABS7
 
1.5%
(Missing)179
38.2%

Length

2021-07-04T20:13:10.243653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:10.367321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a142
49.1%
f72
24.9%
b68
23.5%
abs7
 
2.4%

Most occurring characters

ValueCountFrequency (%)
A149
37.5%
B75
18.9%
F72
18.1%
-56
 
14.1%
+38
 
9.6%
S7
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter303
76.3%
Dash Punctuation56
 
14.1%
Math Symbol38
 
9.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A149
49.2%
B75
24.8%
F72
23.8%
S7
 
2.3%
Math Symbol
ValueCountFrequency (%)
+38
100.0%
Dash Punctuation
ValueCountFrequency (%)
-56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin303
76.3%
Common94
 
23.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A149
49.2%
B75
24.8%
F72
23.8%
S7
 
2.3%
Common
ValueCountFrequency (%)
-56
59.6%
+38
40.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A149
37.5%
B75
18.9%
F72
18.1%
-56
 
14.1%
+38
 
9.6%
S7
 
1.8%

BIO_Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct75
Distinct (%)26.0%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean60.43944637
Minimum0
Maximum95
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:10.510148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.2
Q149
median62
Q375
95-th percentile89
Maximum95
Range95
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.75825654
Coefficient of variation (CV)0.3103644668
Kurtosis-0.3891189436
Mean60.43944637
Median Absolute Deviation (MAD)13
Skewness-0.3849784584
Sum17467
Variance351.8721886
MonotonicityNot monotonic
2021-07-04T20:13:10.653710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4914
 
3.0%
6111
 
2.4%
7110
 
2.1%
7310
 
2.1%
769
 
1.9%
658
 
1.7%
777
 
1.5%
507
 
1.5%
797
 
1.5%
367
 
1.5%
Other values (65)199
42.5%
(Missing)179
38.2%
ValueCountFrequency (%)
01
0.2%
141
0.2%
161
0.2%
171
0.2%
182
0.4%
191
0.2%
211
0.2%
222
0.4%
232
0.4%
251
0.2%
ValueCountFrequency (%)
952
 
0.4%
941
 
0.2%
923
0.6%
913
0.6%
902
 
0.4%
895
1.1%
882
 
0.4%
872
 
0.4%
864
0.9%
854
0.9%

BIO_G.P.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
0.0
71 
4.0
60 
3.0
56 
5.0
52 
3.5
50 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters867
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.5
2nd row4.0
3rd row3.5
4th row0.0
5th row3.5

Common Values

ValueCountFrequency (%)
0.071
 
15.2%
4.060
 
12.8%
3.056
 
12.0%
5.052
 
11.1%
3.550
 
10.7%
(Missing)179
38.2%

Length

2021-07-04T20:13:10.921993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:10.991292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.071
24.6%
4.060
20.8%
3.056
19.4%
5.052
18.0%
3.550
17.3%

Most occurring characters

ValueCountFrequency (%)
0310
35.8%
.289
33.3%
3106
 
12.2%
5102
 
11.8%
460
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number578
66.7%
Other Punctuation289
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0310
53.6%
3106
 
18.3%
5102
 
17.6%
460
 
10.4%
Other Punctuation
ValueCountFrequency (%)
.289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0310
35.8%
.289
33.3%
3106
 
12.2%
5102
 
11.8%
460
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0310
35.8%
.289
33.3%
3106
 
12.2%
5102
 
11.8%
460
 
6.9%

BIO_L.G.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.1%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
F
67 
A
60 
B
56 
A+
52 
A-
50 

Length

Max length3
Median length1
Mean length1.380622837
Min length1

Characters and Unicode

Total characters399
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-
2nd rowA
3rd rowA-
4th rowF
5th rowA-

Common Values

ValueCountFrequency (%)
F67
 
14.3%
A60
 
12.8%
B56
 
12.0%
A+52
 
11.1%
A-50
 
10.7%
ABS4
 
0.9%
(Missing)179
38.2%

Length

2021-07-04T20:13:11.287471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:11.422112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a162
56.1%
f67
23.2%
b56
 
19.4%
abs4
 
1.4%

Most occurring characters

ValueCountFrequency (%)
A166
41.6%
F67
16.8%
B60
 
15.0%
+52
 
13.0%
-50
 
12.5%
S4
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter297
74.4%
Math Symbol52
 
13.0%
Dash Punctuation50
 
12.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A166
55.9%
F67
22.6%
B60
 
20.2%
S4
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
-50
100.0%
Math Symbol
ValueCountFrequency (%)
+52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin297
74.4%
Common102
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A166
55.9%
F67
22.6%
B60
 
20.2%
S4
 
1.3%
Common
ValueCountFrequency (%)
+52
51.0%
-50
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII399
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A166
41.6%
F67
16.8%
B60
 
15.0%
+52
 
13.0%
-50
 
12.5%
S4
 
1.0%

Math_Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct82
Distinct (%)28.4%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean47.16955017
Minimum5
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:11.592656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile16
Q130
median49
Q364
95-th percentile82.6
Maximum95
Range90
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.79685907
Coefficient of variation (CV)0.4408958533
Kurtosis-0.852223021
Mean47.16955017
Median Absolute Deviation (MAD)17
Skewness0.08752390482
Sum13632
Variance432.5093474
MonotonicityNot monotonic
2021-07-04T20:13:11.726298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4912
 
2.6%
558
 
1.7%
427
 
1.5%
447
 
1.5%
637
 
1.5%
597
 
1.5%
256
 
1.3%
666
 
1.3%
526
 
1.3%
286
 
1.3%
Other values (72)217
46.4%
(Missing)179
38.2%
ValueCountFrequency (%)
51
 
0.2%
61
 
0.2%
81
 
0.2%
91
 
0.2%
101
 
0.2%
111
 
0.2%
122
0.4%
131
 
0.2%
142
0.4%
153
0.6%
ValueCountFrequency (%)
951
 
0.2%
941
 
0.2%
921
 
0.2%
911
 
0.2%
864
0.9%
854
0.9%
841
 
0.2%
832
0.4%
822
0.4%
804
0.9%

Math_G.P.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
0.0
144 
3.0
50 
3.5
47 
4.0
27 
5.0
21 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters867
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.5
2nd row3.5
3rd row3.5
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
0.0144
30.8%
3.050
 
10.7%
3.547
 
10.0%
4.027
 
5.8%
5.021
 
4.5%
(Missing)179
38.2%

Length

2021-07-04T20:13:12.008543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:12.091323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0144
49.8%
3.050
 
17.3%
3.547
 
16.3%
4.027
 
9.3%
5.021
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0386
44.5%
.289
33.3%
397
 
11.2%
568
 
7.8%
427
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number578
66.7%
Other Punctuation289
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0386
66.8%
397
 
16.8%
568
 
11.8%
427
 
4.7%
Other Punctuation
ValueCountFrequency (%)
.289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0386
44.5%
.289
33.3%
397
 
11.2%
568
 
7.8%
427
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0386
44.5%
.289
33.3%
397
 
11.2%
568
 
7.8%
427
 
3.1%

Math_L.G.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.1%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
F
121 
B
50 
A-
47 
A
27 
ABS
23 

Length

Max length3
Median length1
Mean length1.394463668
Min length1

Characters and Unicode

Total characters403
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-
2nd rowA-
3rd rowA-
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
F121
25.9%
B50
 
10.7%
A-47
 
10.0%
A27
 
5.8%
ABS23
 
4.9%
A+21
 
4.5%
(Missing)179
38.2%

Length

2021-07-04T20:13:12.389525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:12.497237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
f121
41.9%
a95
32.9%
b50
17.3%
abs23
 
8.0%

Most occurring characters

ValueCountFrequency (%)
F121
30.0%
A118
29.3%
B73
18.1%
-47
 
11.7%
S23
 
5.7%
+21
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter335
83.1%
Dash Punctuation47
 
11.7%
Math Symbol21
 
5.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F121
36.1%
A118
35.2%
B73
21.8%
S23
 
6.9%
Dash Punctuation
ValueCountFrequency (%)
-47
100.0%
Math Symbol
ValueCountFrequency (%)
+21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin335
83.1%
Common68
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
F121
36.1%
A118
35.2%
B73
21.8%
S23
 
6.9%
Common
ValueCountFrequency (%)
-47
69.1%
+21
30.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F121
30.0%
A118
29.3%
B73
18.1%
-47
 
11.7%
S23
 
5.7%
+21
 
5.2%

ICT_Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct70
Distinct (%)24.2%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean64.67820069
Minimum0
Maximum96
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:12.644842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q154
median66
Q378
95-th percentile90
Maximum96
Range96
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.86373829
Coefficient of variation (CV)0.2607329534
Kurtosis-0.01730564706
Mean64.67820069
Median Absolute Deviation (MAD)12
Skewness-0.4484742361
Sum18692
Variance284.385669
MonotonicityNot monotonic
2021-07-04T20:13:12.774449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6612
 
2.6%
6711
 
2.4%
8410
 
2.1%
639
 
1.9%
729
 
1.9%
628
 
1.7%
748
 
1.7%
618
 
1.7%
808
 
1.7%
548
 
1.7%
Other values (60)198
42.3%
(Missing)179
38.2%
ValueCountFrequency (%)
01
 
0.2%
221
 
0.2%
261
 
0.2%
294
0.9%
301
 
0.2%
311
 
0.2%
324
0.9%
331
 
0.2%
342
0.4%
352
0.4%
ValueCountFrequency (%)
961
 
0.2%
951
 
0.2%
942
 
0.4%
936
1.3%
921
 
0.2%
913
0.6%
903
0.6%
892
 
0.4%
884
0.9%
874
0.9%

ICt_G.P.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
5.0
68 
3.5
66 
4.0
58 
3.0
51 
0.0
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters867
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.5
2nd row4.0
3rd row3.5
4th row0.0
5th row3.5

Common Values

ValueCountFrequency (%)
5.068
 
14.5%
3.566
 
14.1%
4.058
 
12.4%
3.051
 
10.9%
0.046
 
9.8%
(Missing)179
38.2%

Length

2021-07-04T20:13:13.031626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:13.119439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
5.068
23.5%
3.566
22.8%
4.058
20.1%
3.051
17.6%
0.046
15.9%

Most occurring characters

ValueCountFrequency (%)
.289
33.3%
0269
31.0%
5134
15.5%
3117
13.5%
458
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number578
66.7%
Other Punctuation289
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0269
46.5%
5134
23.2%
3117
20.2%
458
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.289
33.3%
0269
31.0%
5134
15.5%
3117
13.5%
458
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.289
33.3%
0269
31.0%
5134
15.5%
3117
13.5%
458
 
6.7%

ICT_L.G.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.1%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
A+
68 
A-
66 
A
58 
B
51 
F
42 

Length

Max length3
Median length1
Mean length1.491349481
Min length1

Characters and Unicode

Total characters431
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-
2nd rowA
3rd rowA-
4th rowF
5th rowA-

Common Values

ValueCountFrequency (%)
A+68
 
14.5%
A-66
 
14.1%
A58
 
12.4%
B51
 
10.9%
F42
 
9.0%
ABS4
 
0.9%
(Missing)179
38.2%

Length

2021-07-04T20:13:13.407690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:13.523174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a192
66.4%
b51
 
17.6%
f42
 
14.5%
abs4
 
1.4%

Most occurring characters

ValueCountFrequency (%)
A196
45.5%
+68
 
15.8%
-66
 
15.3%
B55
 
12.8%
F42
 
9.7%
S4
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter297
68.9%
Math Symbol68
 
15.8%
Dash Punctuation66
 
15.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A196
66.0%
B55
 
18.5%
F42
 
14.1%
S4
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
-66
100.0%
Math Symbol
ValueCountFrequency (%)
+68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin297
68.9%
Common134
31.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A196
66.0%
B55
 
18.5%
F42
 
14.1%
S4
 
1.3%
Common
ValueCountFrequency (%)
+68
50.7%
-66
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A196
45.5%
+68
 
15.8%
-66
 
15.3%
B55
 
12.8%
F42
 
9.7%
S4
 
0.9%

APT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)16.0%
Missing186
Missing (%)39.7%
Infinite0
Infinite (%)0.0%
Mean46.54609929
Minimum9
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:13.653009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile32
Q140
median46
Q353
95-th percentile62.95
Maximum73
Range64
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.571588427
Coefficient of variation (CV)0.2056367466
Kurtosis0.3504232302
Mean46.54609929
Median Absolute Deviation (MAD)6
Skewness0.0174031445
Sum13126
Variance91.61530502
MonotonicityNot monotonic
2021-07-04T20:13:13.775983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5021
 
4.5%
4219
 
4.1%
3817
 
3.6%
4314
 
3.0%
4713
 
2.8%
5113
 
2.8%
4111
 
2.4%
5210
 
2.1%
469
 
1.9%
449
 
1.9%
Other values (35)146
31.2%
(Missing)186
39.7%
ValueCountFrequency (%)
91
 
0.2%
241
 
0.2%
271
 
0.2%
283
 
0.6%
293
 
0.6%
303
 
0.6%
312
 
0.4%
323
 
0.6%
334
0.9%
348
1.7%
ValueCountFrequency (%)
732
 
0.4%
711
 
0.2%
681
 
0.2%
665
1.1%
652
 
0.4%
641
 
0.2%
633
0.6%
621
 
0.2%
613
0.6%
605
1.1%

APT_G.P.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.4%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
0.0
170 
3.0
88 
3.5
28 
4.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters867
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.5
2nd row3.0
3rd row3.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0170
36.3%
3.088
18.8%
3.528
 
6.0%
4.03
 
0.6%
(Missing)179
38.2%

Length

2021-07-04T20:13:14.032673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:14.101069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0170
58.8%
3.088
30.4%
3.528
 
9.7%
4.03
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0431
49.7%
.289
33.3%
3116
 
13.4%
528
 
3.2%
43
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number578
66.7%
Other Punctuation289
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0431
74.6%
3116
 
20.1%
528
 
4.8%
43
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0431
49.7%
.289
33.3%
3116
 
13.4%
528
 
3.2%
43
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0431
49.7%
.289
33.3%
3116
 
13.4%
528
 
3.2%
43
 
0.3%

APT_L.G.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.4%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
F
170 
B
88 
A-
28 
A
 
3

Length

Max length2
Median length1
Mean length1.096885813
Min length1

Characters and Unicode

Total characters317
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-
2nd rowB
3rd rowB
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F170
36.3%
B88
18.8%
A-28
 
6.0%
A3
 
0.6%
(Missing)179
38.2%

Length

2021-07-04T20:13:14.316315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:14.378800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
f170
58.8%
b88
30.4%
a31
 
10.7%

Most occurring characters

ValueCountFrequency (%)
F170
53.6%
B88
27.8%
A31
 
9.8%
-28
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter289
91.2%
Dash Punctuation28
 
8.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F170
58.8%
B88
30.4%
A31
 
10.7%
Dash Punctuation
ValueCountFrequency (%)
-28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin289
91.2%
Common28
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
F170
58.8%
B88
30.4%
A31
 
10.7%
Common
ValueCountFrequency (%)
-28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F170
53.6%
B88
27.8%
A31
 
9.8%
-28
 
8.8%

Grand Total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct202
Distinct (%)69.9%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean364.2456747
Minimum100
Maximum585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:14.746098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile204.8
Q1299
median366
Q3433
95-th percentile514.2
Maximum585
Range485
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.42606655
Coefficient of variation (CV)0.2647281031
Kurtosis-0.3209065981
Mean364.2456747
Median Absolute Deviation (MAD)67
Skewness-0.1252499247
Sum105267
Variance9297.986309
MonotonicityNot monotonic
2021-07-04T20:13:14.880735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4925
 
1.1%
3674
 
0.9%
3744
 
0.9%
4094
 
0.9%
3094
 
0.9%
3784
 
0.9%
3483
 
0.6%
4453
 
0.6%
3143
 
0.6%
3373
 
0.6%
Other values (192)252
53.8%
(Missing)179
38.2%
ValueCountFrequency (%)
1001
0.2%
1281
0.2%
1361
0.2%
1371
0.2%
1451
0.2%
1481
0.2%
1551
0.2%
1731
0.2%
1741
0.2%
1801
0.2%
ValueCountFrequency (%)
5851
0.2%
5831
0.2%
5761
0.2%
5731
0.2%
5691
0.2%
5581
0.2%
5451
0.2%
5431
0.2%
5422
0.4%
5321
0.2%

TTL_GP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct25
Distinct (%)8.7%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean1.923321799
Minimum0
Maximum5
Zeros153
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:14.998420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile4.884
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.078068356
Coefficient of variation (CV)1.080457965
Kurtosis-1.840702053
Mean1.923321799
Median Absolute Deviation (MAD)0
Skewness0.2169295267
Sum555.84
Variance4.318368094
MonotonicityNot monotonic
2021-07-04T20:13:15.109096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0153
32.7%
514
 
3.0%
4.4210
 
2.1%
3.9210
 
2.1%
3.59
 
1.9%
4.178
 
1.7%
4.337
 
1.5%
3.257
 
1.5%
3.837
 
1.5%
3.337
 
1.5%
Other values (15)57
 
12.2%
(Missing)179
38.2%
ValueCountFrequency (%)
0153
32.7%
31
 
0.2%
3.173
 
0.6%
3.257
 
1.5%
3.337
 
1.5%
3.422
 
0.4%
3.59
 
1.9%
3.586
 
1.3%
3.673
 
0.6%
3.755
 
1.1%
ValueCountFrequency (%)
514
3.0%
4.921
 
0.2%
4.833
 
0.6%
4.756
1.3%
4.673
 
0.6%
4.583
 
0.6%
4.55
 
1.1%
4.4210
2.1%
4.337
1.5%
4.255
 
1.1%

TTL_L.G.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
F
153 
A
62 
A-
40 
B
20 
A+
 
14

Length

Max length2
Median length1
Mean length1.186851211
Min length1

Characters and Unicode

Total characters343
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA-
3rd rowA-
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F153
32.7%
A62
 
13.2%
A-40
 
8.5%
B20
 
4.3%
A+14
 
3.0%
(Missing)179
38.2%

Length

2021-07-04T20:13:15.351448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-04T20:13:15.423256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
f153
52.9%
a116
40.1%
b20
 
6.9%

Most occurring characters

ValueCountFrequency (%)
F153
44.6%
A116
33.8%
-40
 
11.7%
B20
 
5.8%
+14
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter289
84.3%
Dash Punctuation40
 
11.7%
Math Symbol14
 
4.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F153
52.9%
A116
40.1%
B20
 
6.9%
Dash Punctuation
ValueCountFrequency (%)
-40
100.0%
Math Symbol
ValueCountFrequency (%)
+14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin289
84.3%
Common54
 
15.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
F153
52.9%
A116
40.1%
B20
 
6.9%
Common
ValueCountFrequency (%)
-40
74.1%
+14
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F153
44.6%
A116
33.8%
-40
 
11.7%
B20
 
5.8%
+14
 
4.1%

No.s of F
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct7
Distinct (%)2.4%
Missing179
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean1.477508651
Minimum0
Maximum6
Zeros136
Zeros (%)29.1%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2021-07-04T20:13:15.504901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.823937863
Coefficient of variation (CV)1.234468483
Kurtosis-0.01903999775
Mean1.477508651
Median Absolute Deviation (MAD)1
Skewness1.061424147
Sum427
Variance3.326749327
MonotonicityNot monotonic
2021-07-04T20:13:15.600264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0136
29.1%
143
 
9.2%
234
 
7.3%
330
 
6.4%
417
 
3.6%
516
 
3.4%
613
 
2.8%
(Missing)179
38.2%
ValueCountFrequency (%)
0136
29.1%
143
 
9.2%
234
 
7.3%
330
 
6.4%
417
 
3.6%
516
 
3.4%
613
 
2.8%
ValueCountFrequency (%)
613
 
2.8%
516
 
3.4%
417
 
3.6%
330
 
6.4%
234
 
7.3%
143
 
9.2%
0136
29.1%

Sec_Position
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)9.0%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
--
153 
1
 
10
2
 
10
5
 
10
3
 
10
Other values (21)
96 

Length

Max length2
Median length2
Mean length1.712802768
Min length1

Characters and Unicode

Total characters495
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.4%

Sample

1st row2
2nd row5
3rd row4
4th row--
5th row--

Common Values

ValueCountFrequency (%)
--153
32.7%
110
 
2.1%
210
 
2.1%
510
 
2.1%
310
 
2.1%
79
 
1.9%
49
 
1.9%
69
 
1.9%
108
 
1.7%
98
 
1.7%
Other values (16)53
 
11.3%
(Missing)179
38.2%

Length

2021-07-04T20:13:15.869407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
153
52.9%
110
 
3.5%
510
 
3.5%
210
 
3.5%
310
 
3.5%
49
 
3.1%
69
 
3.1%
79
 
3.1%
108
 
2.8%
98
 
2.8%
Other values (16)53
 
18.3%

Most occurring characters

ValueCountFrequency (%)
-306
61.8%
163
 
12.7%
226
 
5.3%
315
 
3.0%
514
 
2.8%
414
 
2.8%
712
 
2.4%
612
 
2.4%
911
 
2.2%
011
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation306
61.8%
Decimal Number189
38.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
163
33.3%
226
13.8%
315
 
7.9%
514
 
7.4%
414
 
7.4%
712
 
6.3%
612
 
6.3%
911
 
5.8%
011
 
5.8%
811
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
-306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-306
61.8%
163
 
12.7%
226
 
5.3%
315
 
3.0%
514
 
2.8%
414
 
2.8%
712
 
2.4%
612
 
2.4%
911
 
2.2%
011
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-306
61.8%
163
 
12.7%
226
 
5.3%
315
 
3.0%
514
 
2.8%
414
 
2.8%
712
 
2.4%
612
 
2.4%
911
 
2.2%
011
 
2.2%

Position
Categorical

HIGH CARDINALITY
MISSING

Distinct135
Distinct (%)46.7%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
--
153 
6
 
2
20
 
2
19
 
1
21
 
1
Other values (130)
130 

Length

Max length3
Median length2
Mean length2.08650519
Min length1

Characters and Unicode

Total characters603
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)45.7%

Sample

1st row66
2nd row86
3rd row80
4th row--
5th row--

Common Values

ValueCountFrequency (%)
--153
32.7%
62
 
0.4%
202
 
0.4%
191
 
0.2%
211
 
0.2%
1301
 
0.2%
1121
 
0.2%
21
 
0.2%
771
 
0.2%
961
 
0.2%
Other values (125)125
26.7%
(Missing)179
38.2%

Length

2021-07-04T20:13:16.135509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
153
52.9%
202
 
0.7%
62
 
0.7%
1251
 
0.3%
641
 
0.3%
851
 
0.3%
1181
 
0.3%
621
 
0.3%
211
 
0.3%
221
 
0.3%
Other values (125)125
43.3%

Most occurring characters

ValueCountFrequency (%)
-306
50.7%
169
 
11.4%
235
 
5.8%
329
 
4.8%
624
 
4.0%
024
 
4.0%
424
 
4.0%
823
 
3.8%
923
 
3.8%
523
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation306
50.7%
Decimal Number297
49.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
169
23.2%
235
11.8%
329
9.8%
624
 
8.1%
024
 
8.1%
424
 
8.1%
823
 
7.7%
923
 
7.7%
523
 
7.7%
723
 
7.7%
Dash Punctuation
ValueCountFrequency (%)
-306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-306
50.7%
169
 
11.4%
235
 
5.8%
329
 
4.8%
624
 
4.0%
024
 
4.0%
424
 
4.0%
823
 
3.8%
923
 
3.8%
523
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-306
50.7%
169
 
11.4%
235
 
5.8%
329
 
4.8%
624
 
4.0%
024
 
4.0%
424
 
4.0%
823
 
3.8%
923
 
3.8%
523
 
3.8%

ATP_Position
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)7.3%
Missing179
Missing (%)38.2%
Memory size3.8 KiB
--
173 
20
21 
19
 
13
18
 
10
17
 
8
Other values (16)
64 

Length

Max length2
Median length2
Mean length1.944636678
Min length1

Characters and Unicode

Total characters562
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)2.1%

Sample

1st row11
2nd row12
3rd row19
4th row--
5th row--

Common Values

ValueCountFrequency (%)
--173
37.0%
2021
 
4.5%
1913
 
2.8%
1810
 
2.1%
178
 
1.7%
158
 
1.7%
108
 
1.7%
167
 
1.5%
127
 
1.5%
117
 
1.5%
Other values (11)27
 
5.8%
(Missing)179
38.2%

Length

2021-07-04T20:13:16.392826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
173
59.9%
2021
 
7.3%
1913
 
4.5%
1810
 
3.5%
158
 
2.8%
178
 
2.8%
108
 
2.8%
127
 
2.4%
117
 
2.4%
167
 
2.4%
Other values (11)27
 
9.3%

Most occurring characters

ValueCountFrequency (%)
-346
61.6%
188
 
15.7%
229
 
5.2%
029
 
5.2%
914
 
2.5%
813
 
2.3%
410
 
1.8%
59
 
1.6%
79
 
1.6%
68
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation346
61.6%
Decimal Number216
38.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
188
40.7%
229
 
13.4%
029
 
13.4%
914
 
6.5%
813
 
6.0%
410
 
4.6%
59
 
4.2%
79
 
4.2%
68
 
3.7%
37
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
-346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common562
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-346
61.6%
188
 
15.7%
229
 
5.2%
029
 
5.2%
914
 
2.5%
813
 
2.3%
410
 
1.8%
59
 
1.6%
79
 
1.6%
68
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-346
61.6%
188
 
15.7%
229
 
5.2%
029
 
5.2%
914
 
2.5%
813
 
2.3%
410
 
1.8%
59
 
1.6%
79
 
1.6%
68
 
1.4%

Interactions

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Correlations

2021-07-04T20:13:16.513498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-04T20:13:16.809683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-04T20:13:17.105356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-04T20:13:17.427325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-04T20:13:17.821169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-04T20:13:00.408293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-04T20:13:02.118340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-07-04T20:13:02.810152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-07-04T20:13:04.802948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ID No.Name EnglishSection4th SubBan_TotalBan_G.P.Ban_L.G.Eng_TotalEng_G.P.Eng_L.G.Phy_TotalPhy_G.P.Phy_L.G.Che_TotalChe_G.P.Che_L.G.BIO_TotalBIO_G.P.BIO_L.G.Math_TotalMath_G.P.Math_L.G.ICT_TotalICt_G.P.ICT_L.G.APTAPT_G.P.APT_L.G.Grand TotalTTL_GPTTL_L.G.No.s of FSec_PositionPositionATP_Position
02190001.0SAIMA SHARMIN MONISHAShankhamalaAgri65.03.5A-61.03.5A-92.05.0A+79.05.0A+68.03.5A-67.03.5A-63.03.5A-59.03.5A-445.04.08A0.026611
12190002.0MOSSAMAT NUSAIBA ISLAMShankhamalaAgri77.04.0A57.03.0B70.04.0A68.03.5A-69.04.0A68.03.5A-71.04.0A58.03.0B430.03.83A-0.058612
22190004.0TAHMINA AFROJ RITUShankhamalaAgri69.04.0A50.03.0B79.05.0A+73.04.0A66.03.5A-67.03.5A-62.03.5A-51.03.0B416.03.92A-0.048019
32190005.0ANUPOMA DEBSHARMAShankhamalaAgri53.03.0B44.00.0F65.03.5A-37.00.0F44.00.0F56.03.0B46.00.0F41.00.0F295.00.00F4.0------
42190007.0MARIA AKTER MIMShankhamalaAgri63.03.5A-44.00.0F84.05.0A+68.03.5A-64.03.5A-71.04.0A67.03.5A-47.00.0F411.00.00F1.0------
52190008.0ZARIN TASNIMShankhamalaAgri71.04.0A68.03.5A-75.04.0A67.03.5A-66.03.5A-82.05.0A+67.03.5A-59.03.5A-446.04.00A0.036911
62190011.0MST. SABIHA SULTANA RIMAShankhamalaAgri49.03.0B42.00.0F57.03.0B58.03.0B47.00.0F61.03.5A-36.00.0F42.00.0F300.00.00F3.0------
72190013.0MUSFEKA IFFAT SENINShankhamalaAgri64.03.5A-50.03.0B97.05.0A+80.05.0A+82.05.0A+64.03.5A-78.04.0A52.03.0B465.04.33A0.014818
82190019.0ADDOSHI SHIARA RAKHIShankhamalaAgri56.03.0B49.03.0B90.05.0A+63.03.5A-52.03.0B58.03.0B55.03.0B50.03.0B373.03.42B0.0711620
92190020.0MAHABUBA AKTER OISHIShankhamalaAgri56.03.0B43.00.0F94.05.0A+68.03.5A-72.04.0A59.03.5A-63.03.5A-73.04.0A405.00.00F1.0----1

Last rows

ID No.Name EnglishSection4th SubBan_TotalBan_G.P.Ban_L.G.Eng_TotalEng_G.P.Eng_L.G.Phy_TotalPhy_G.P.Phy_L.G.Che_TotalChe_G.P.Che_L.G.BIO_TotalBIO_G.P.BIO_L.G.Math_TotalMath_G.P.Math_L.G.ICT_TotalICt_G.P.ICT_L.G.APTAPT_G.P.APT_L.G.Grand TotalTTL_GPTTL_L.G.No.s of FSec_PositionPositionATP_Position
458NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
459NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
460NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
461NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
462NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
463NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
464NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
465NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
466NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
467NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN